Optimising a stochastic dynamic neural tree

This paper describes experiments performed using a genetic algorithm (GA) to optimise the parameters of a novel model of a stochastic hierarchical neural clusterer. Two issues of enhancing and optimising the model are discussed. Two fitness functions were created from two selected clustering measures, and a population of genotypes, specifying parameters of the model were evolved. Using the idea of optimising the model by a GA has been proven to be useful. This process mirrors genomic evolution and ontogeny.